Fast style transfer for ethnic patterns innovation

Abstract

Minority ethnic patterns in China, reflecting diverse cultures, serve as symbols of ethnic identity and cultural heritage. However, the current artistic domain of minority ethnic patterns faces dual challenges: limited artistic styles and insufficient innovation efficiency. In addressing these issues, this study proposes a novel approach for ethnic pattern innovation, named Fast Style Transfer for Ethnic Pattern Innovation (FST-EPI), aiming to enhance innovation efficiency and diversify artistic styles. FST-EPI comprises three key modules: background generation, background fusion, and fast transfer. Firstly, the background generation module utilizes the Visual Geometry Group network (VGGNet) to extract features from images, generating a background image with the desired style while preserving the necessary stylistic attributes. Subsequently, textile patterns are fused with the stylized background image, forming a content-background fusion image. Finally, the selected stylistic patterns, along with the content-background fusion image, are input into the fast transfer module to produce the ultimate innovative pattern. Experimental results demonstrate that the generated images not only fully retain the core features and cultural elements of minority ethnic patterns but also successfully integrate the unique styles from one or more reference images. This proposed method exhibits outstanding efficiency and feasibility in a broad range of experiments related to ethnic pattern style transfer, as evidenced by thorough comparisons with state-of-the-art methods. Ultimately, the processing speed of this method achieves a real-time output at the level of seconds, reducing processing time by nearly 50% compared to traditional approaches.